similar to: how to use large data set ?

Thank you for your answer but I already use the .deb package. Also I have compiled the source code, but it is the same result... I have already the same error.. I 'm going to be crazy ;-) Has anyone got the same problem (and found the solution!) ? Thanks in advance Yohan -----Message d'origine----- De : Dirk Eddelbuettel [mailto:edd@debian.org] Envoyé : jeudi 10 août 2006 18:50 À :

R-users, Sorry for my English, I'm French. I want to use the Rcmdr package which depends on the tcltk library. I'm on Linux Ubuntu. I don't manage to use Rcmdr even if tcl and tk are installed. I'm a newbie in Linux and I don't know how to specify Configtcl.sh and Configtk.sh location to R (I saw that on the mailing list archive). I have installed all the

can any one help me to now , what is the result of this code ? #start edw<-function(mm,n){ v2<-v3<-0 d<-dim(mm) dd<-d[1]*d[2] p1<-2*n/(dd*(dd-n)) p2<-(n*(n-1)*(dd-n)+2*n*(dd-d[1]-d[2]-n+2))/(dd-d[1]-d[2]+1)*(dd-n) v1<-((dd-n-1)/(n+1))*sum(mm^2) c1<-(dd^2*p1)/(n+1)^2-1 c2<-(dd^2*p2)/(n+1)^2-1 for(i in 1:d[1]){ for(j in 1:d[2]){

Dear all, I've encountered a curious problem. I am trying to run an SQL query using sqlQuery() function in RODBC. The query works fine when run in a stand-alone SQL browser (Microsoft Query Analyzer, in particular). However, when I use the exact same thing from sqlQuery() function, I get the following error: Error in "[.data.frame"(data, , ) : not all specified columns exist

Hello folks, I am very excited to have discovered R and have been exploring its capabilities. R's regression models are of great interest to me as my company is in the business of running thousands of linear regressions on large datasets. I am using biglm to run linear regressions on datasets that are as large as several GB's. I have been pleasantly surprised that biglm runs the

Hi, I'm trying to compile the package biglm, but when I build it with R CMD build biglm, it failed : C:\LOCAL\c-dutang\code\R\biglm2>R CMD build biglm * checking for file 'biglm/DESCRIPTION' ... OK * preparing 'biglm': * checking DESCRIPTION meta-information ...C:/DOCUME~1/c-dutang/Local: Can't op n C:/DOCUME~1/c-dutang/Local: No such file or directory

I am trying to figure out why 'biglm' can handle large data set... According to the R document - "biglm creates a linear model object that uses only p^2 memory for p variables. It can be updated with more data using update. This allows linear regression on data sets larger than memory." After reading the source code below? I still could not figure out how 'update'

If a package defined an S3 generic and an S4 generic for the same function (so as to add methods for S4 classes to the existing code), how do I set up the namespace to have them exported? With import(stats) exportMethods(bigglm) importClassesFrom(DBI) useDynLib(biglm) export(biglm) export(bigglm) in NAMESPACE, the S3 generic is not exported. > methods("bigglm") [1] bigglm.RODBC*

Dear all, While debugging a function I realized that sort(c(" 1", " 2", "10")) do not give the same result on Windows and Linux. This is actually not surprising because white spaces are not handle in the same manner on these two platforms. But I was wondering if this behavior is also desired in R. regards, Yohan Chalabi

Hi room, There seems to be a problem with the Windows building machines of R-Forge. All our packages with Fortran source code cannot be compiled for Windows. The error in the log file is make[3]: gfortran: Command not found It seems that gfortran is not installed. Is there any plan to fix this or am I doing something wrong on R-Forge? thanks in advance for your advises. regards, Yohan --

Dear all, I am working on a large dataset and need to use biglm() to perform OLS regressions. I have detected significant ARCH effects which I try to account for using the Newey-West correction. So far, I have worked with NeweyWest() in the sandwich package. NeweyWest() however seems to be unable to handle an object of class "biglm". Looking into the code, I figured out that

Hello colleagues, I have tried to use the package biglm. I want to specify a multivariate regression with a weight. I have imported a large dataset with the library(bigmemory). I load the library (biglm) and specified a regression with a weight. But I get everytime a error message like ?object not found? or ?`weights' must be a formula? or "error in eval(expr, envir, enclos)". I

It seems like everyone now has a RoR type framework: Perl: http://dev.catalyst.perl.org/ PHP: http://cakephp.org/ Python: http://www.django.com ASP.NET: http://www.castleproject.org/index.php/Main_Page etc., etc. Has a revolution begun with the birth of these "Web 2.0" frameworks? Its going to be interesting when the tech media begins its mass hype like it did SOAP, XML, etc. a

Hi Sir, Thanks for making package available to us. I am facing few problems if you can give some hints: Problem-1: The model summary and residual deviance matched (in the mail below) but I didn't understand why AIC is still different. > AIC(m1) [1] 532965 > AIC(m1big_longer) [1] 101442.9 Problem-2: chunksize argument is there in bigglm but not in biglm, consequently,

Hello. I had been struggling with running linear regression using lm() primarily because my data has a few categorical variables with at least a thousand levels. I tried the biglm() function and it worked. My problem now is that i don't know how to get the R2 results. Could someone help? Thanks, sean

Hi all - I'm trying to find a way to create dummy variables from factors in a regression. I have been using biglm along the lines of ff <- log(Price) ~ factor(Colour):factor(Store) + factor(DummyVar):factor(Colour):factor(Store) lm1 <- biglm(ff, data=my.dataset) but because there are lots of colours (>100) and lots of stores (>250), I run it to memory problems. Now, not every

I've been working with a rather large data set (~10M rows), and while biglm works beautifully for generating coefficients, it does not report an r-squared. It does report RSS. Any idea on how one could coax an R-squared out of biglm? Thanks in advance for any help with this! Bryan Lim Lecturer Department of Finance University of Melbourne [[alternative HTML version deleted]]

I'm looking for a formula for memory usage in standard regression; that is, if I have X rows with Y predictors, how much memory is needed? I'm speccing out a system, and I'd like to be able to get enough memory that we can do some fairly large regressions. ==Ed Freeman [[alternative HTML version deleted]]

Hello dear R mailing list members. I have recently became curious of the possibility applying model selection algorithms (even as simple as AIC) to regressions of large datasets. I searched as best as I could, but couldn't find any reference or wrapper for using step or stepAIC to packages such as biglm. Any ideas or directions of how to implement such a concept ? Best, Tal --

I'm tryuing to get the RODBC package to work with a MS Sql database (Wonderware's InSQL). I'm able to connect to the database using the following: version _ platform i386-pc-mingw32 arch x86 os Win32 system x86, Win32 status Under development (unstable)